Best local AI models for AMD R7 M460

The largest models that fit fully

The 30 largest of the 56 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The AMD Radeon R7 M460 is an entry level laptop graphics card equipped with 2 GB of GDDR5 dedicated video memory. This memory capacity dictates the size of the artificial intelligence models you can run locally. To load a model entirely onto the graphics processor, the model files and active memory must fit within this 2 GB limit. Running models locally on your hardware ensures complete privacy and eliminates subscription costs.

To fit models onto this hardware, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quant uses a four bit compression method to reduce file size while preserving accuracy. As models get smaller, you can use higher quality quants like Q6_K or Q8_0. These higher quants use six or eight bits per weight, which increases precision but requires more memory space.

With 2 GB of video memory, the largest fully fitting models include Allegro 2.8B and Open-Sora Plan 2.7B at a Q4_K_M quant, which use exactly 2 GB. You can also run LFM2 2.6B and Playground v2.5 at Q4_K_M, using 1.9 GB. Stable Diffusion 3.5 Medium and Canary 2.5B fit at Q4_K_M using 1.8 GB. For higher precision, you can run SmolVLM 2B or Stable Diffusion 3 Medium at Q6_K using 2 GB, or Whisper Large v3 1.55B at Q8_0 using 2 GB.

If you want to run larger models, you must use CPU offload. This technique splits the workload between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these setups. Offloading allows you to run SmolLM3 3B, Replit Code v1.5 3B, or Kandinsky 3.1. These models need 2.2 GB of video memory at Q4_K_M and require 4.2 GB of system RAM. You can also run SDXL Turbo 3.5B, which needs 2.6 GB of video memory at Q4_K_M and 4.6 GB of system RAM.

CPU offload comes with a performance cost. Transferring data between the graphics card and system RAM is much slower than keeping everything inside the GDDR5 memory. This transfer delay reduces the generation speed significantly. In addition, these memory calculations assume a standard 4k context window. If you increase the context length to process longer text, the memory usage will rise and may exceed your limits.